Alkalmazott Matematikai és Valószínűségszámítási Tanszék

1.

Martos, R., Szokol, Á., Veres, M., Gáll, J., Csik, A., Hegedűs, C., Keczánné Üveges, A., Tóth, E. R., Szalóki, M.: Evaluation of Two-Body Wear of Nanocomposites With Different Filler Morphology and Composition Using White-Light Interferometry.
Int J Dent. 2026, 1-14, (cikkazonosító: 8929009), 2026.

doi DEA

Folyóirat-mutatók:

Q1 Dentistry (miscellaneous) (2024)

2.

Mayer, M. J., Baran, Á., Lerch, S., Horat, N., Yang, D., Baran, S.: Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods.
Sol. Energy. 307, 1-36, (cikkazonosító: 114361), 2026.

doi DEA

Folyóirat-mutatók:

Q1 Materials Science (miscellaneous) (2024)

Q1 Renewable Energy, Sustainability and the Environment (2024)

3.

Mayer, M. J., Baran, S.: Probabilistic PV forecasting.
In: AI-Based Forecasting of Solar Photovoltaics Power Generation / Elham Shirazi; Wilfried van Sark (eds), The Institution of Engineering and Technology (The IET), London, 149-176, 2026. ISBN: 9781837240197

doi DEA

4.

Fazekas, I., Fórián, L.: A family of network evolution models with moderate density.
Stat. Pap. 66 (5), 1-18, (cikkazonosító: 105), 2025.

doi DEA WoS

Folyóirat-mutatók:

Q2 Statistics and Probability (2024)

Q2 Statistics, Probability and Uncertainty (2024)

5.

Rácz, A.: A MILP model for one dimensional cutting stock problem with adjustable leftover threshold and cutting cost.
IJOCTA. 15 (2), 215-224, 2025.

doi DEA

Folyóirat-mutatók:

Q2 Applied Mathematics (2024)

Q3 Control and Optimization (2024)

6.

Leutbecher, M., Baran, S.: Ensemble size dependence of the logarithmic score for forecasts issued as multivariate normal distributions.
Q. J. R. Meteorol. Soc. 151 (766), 1-16, (cikkazonosító: 4898), 2025.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q1 Atmospheric Science (2024)

7.

Baran, S., Marín, J. C., Cuevas, O., Díaz, M., Szabó, M., Nicolis, O., Nagy-Lakatos, M.: Machine learning-based probabilistic forecasting of solar irradiance in Chile.
Adv. Stat. Clim. Meteorol. Oceanogr. 11 (1), 89-105, 2025.

doi DEA Scopus

Folyóirat-mutatók:

Q2 Applied Mathematics (2024)

Q3 Atmospheric Science (2024)

Q2 Oceanography (2024)

Q2 Statistics and Probability (2024)

8.

Nagy, B. Á., Nagy, B. Á., Nagy, Á., Gáll, J., Sterbenz, T.: Offensive efficiency and traditional positional roles in Hungarian basketball: an empirical analysis.
Front. Sports Act. Living. 7, 01-12, (cikkazonosító: 1658662), 2025.

doi DEA

Folyóirat-mutatók:

D1 Anthropology (2024)

Q2 Orthopedics and Sports Medicine (2024)

Q1 Physical Therapy, Sports Therapy and Rehabilitation (2024)

Q2 Physiology (2024)

Q2 Public Health, Environmental and Occupational Health (2024)

Q2 Tourism, Leisure and Hospitality Management (2024)

9.

Rácz, A.: Optimization model for reducing inconsistency of pairwise comparison matrices with minimal change.
OPSEARCH. 62, 2272-2288, 2025.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q3 Computer Science Applications (2024)

Q3 Information Systems (2024)

Q2 Management Information Systems (2024)

Q3 Management Science and Operations Research (2024)

10.

Nagy, B. Á., Nagy, B. Á., Nagy, Á., Gáll, J., Sterbenz, T.: Rethinking the Strongest Link: VAL, Ratings, and Team Success in Hungarian Basketball.
Front. Sports Act. Living. 7, 1-12, (cikkazonosító: 1658676), 2025.

doi DEA

Folyóirat-mutatók:

D1 Anthropology (2024)

Q2 Orthopedics and Sports Medicine (2024)

Q1 Physical Therapy, Sports Therapy and Rehabilitation (2024)

Q2 Physiology (2024)

Q2 Public Health, Environmental and Occupational Health (2024)

Q2 Tourism, Leisure and Hospitality Management (2024)

11.

Fazekas, I., Barta, A., Fórián, L., Porvázsnyik, B.: A continuous-time network evolution model describing N-interactions.
MATH. 9 (12), 35721-35742, 2024.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q2 Mathematics (miscellaneous)

12.

Lakatos, R., Bogacsovics, G., Harangi, B., Lakatos, I., Tiba, A., Tóth, J., Szabó, M., Hajdu, A.: A Machine Learning-Based Pipeline for the Extraction of Insights from Customer Reviews.
Big Data Cogn. Comput. 8 (3), 1-24, 2024.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q2 Artificial Intelligence

Q1 Computer Science Applications

Q1 Information Systems

Q1 Management Information Systems

13.

Baran, Á., Baran, S.: A two-step machine learning approach to statistical post-processing of weather forecasts for power generation.
Q. J. R. Meteorol. Soc. 150 (759), 1029-1047, 2024.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q1 Atmospheric Science

14.

Baran, S., Nagy-Lakatos, M.: Clustering-based spatial interpolation of parametric post-processing models.
Weather Forecast. 39 (11), 1591-1604, 2024.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q1 Atmospheric Science

15.

Antal, G., Szabó, S., Szarvas, P., Pusztahelyi, T., Gáll, J., Holb, I.: Cost-Benefit Analysis of Apple Scab Sanitation Practices for ULO-Stored Apple Fruit in Integrated and Organic Production Systems.
International Journal of Fruit Science. 24 (1), 1-17, 2024.

doi DEA

Folyóirat-mutatók:

Q2 Agronomy and Crop Science

Q2 Ecology

Q2 Plant Science

Q1 Horticulture

16.

Nagy-Lakatos, M., Baran, S.: Enhancing multivariate post-processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts.
Meteorol. Appl. 31 (6), 1-19, (cikkazonosító: 70015), 2024.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q2 Atmospheric Science

17.

Fazekas, I., Barta, A., Noszály, C., Porvázsnyik, B.: A continuous-time network evolution model describing 3-interactions.
Commun. Stat.-Theory Methods. 52 (11), 4001-4020, 2023.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q3 Statistics and Probability

18.

Nagy-Lakatos, M., Lerch, S., Hemri, S., Baran, S.: Comparison of multivariate post-processing methods using global ECMWF ensemble forecasts.
Q. J. R. Meteorol. Soc. 149 (752), 856-877, 2023.

doi DEA WoS Scopus

Folyóirat-mutatók:

D1 Atmospheric Science

19.

Harangi, B., Baran, Á., Beregi-Kovács, M., Hajdu, A.: Composing Diverse Ensembles of Convolutional Neural Networks by Penalization.
Mathematics. 11 (23), 1-19, (cikkazonosító: 4730), 2023.

doi DEA Scopus

Folyóirat-mutatók:

Q2 Computer Science (miscellaneous)

Q2 Engineering (miscellaneous)

Q2 Mathematics (miscellaneous)

20.

Fazekas, I., Fórián, L., Barta, A.: Deep Learning from Noisy Labels with Some Adjustments of a Recent Method.
Infocommun. J. 15 (Special), 9-12, 2023.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q3 Computer Science (miscellaneous)

Q3 Electrical and Electronic Engineering

21.

Szabó, M., Gascón, E., Baran, S.: Parametric Postprocessing of Dual-Resolution Precipitation Forecasts.
Weather Forecast. 38 (8), 1313-1322, 2023.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q1 Atmospheric Science

22.

Baran, S., Nagy-Lakatos, M.: Statistical post-processing of visibility ensemble forecasts.
Meteorol. Appl. 30 (5), 1-18, (cikkazonosító: 2157), 2023.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q2 Atmospheric Science

23.

Szépszó, G., Baran, Á., Baran, S., Jávorné, R. K., Kornyik, M., Tajti, D.: Sugárzásra és magassági szélre vonatkozó rövidtávú előrejelzések operatív statisztikai utófeldolgozása.
Légkör. 68 (3), 118-125, 2023.

doi DEA

24.

Petneházi, G., Gáll, J.: Testing the Lee-Carter model on Hungarian mortality data.
Acta Oecon. 73 (1), 171-182, 2023.

doi DEA WoS

Folyóirat-mutatók:

Q4 Economics and Econometrics

25.

Fazekas, I.: 2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS): Proceedings: May 16-18, 2022. : Online on CISCO Webex platform.
Institute of Electrical and Electronics Engineers (IEEE), Piscataway, 337 p., 2022. ISBN: 9781665496537

DEA

Alkalmazott Matematikai és Valószínűségszámítási Tanszék

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